A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression

Kanokrat Baisad, Nawinda Chutsagulprom and Sompop Moonchai ()
Additional contact information
Kanokrat Baisad: Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Nawinda Chutsagulprom: Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Sompop Moonchai: Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand

Mathematics, 2023, vol. 11, issue 23, 1-18

Abstract: Spatial interpolation of meteorological data can have immense implications on risk management and climate change planning. Kriging with external drift (KED) is a spatial interpolation variant that uses auxiliary information in the estimation of target variables at unobserved locations. However, traditional KED methods with linear trend functions may not be able to capture the complex and non-linear interdependence between target and auxiliary variables, which can lead to an inaccurate estimation. In this work, a novel KED method using least squares support vector regression (LSSVR) is proposed. This machine learning algorithm is employed to construct trend functions regardless of the type of variable interrelations being considered. To evaluate the efficiency of the proposed method (KED with LSSVR) relative to the traditional method (KED with a linear trend function), a systematic simulation study for estimating the monthly mean temperature and pressure in Thailand in 2017 was conducted. The KED with LSSVR is shown to have superior performance over the KED with the linear trend function.

Keywords: geostatistics; spatial interpolation; kriging with external drift; least squares support vector regression; trend function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/23/4799/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/23/4799/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:23:p:4799-:d:1289332

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().